{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-05-21T01:26:56.626382Z","iopub.execute_input":"2023-05-21T01:26:56.627006Z","iopub.status.idle":"2023-05-21T01:27:00.537833Z","shell.execute_reply.started":"2023-05-21T01:26:56.626949Z","shell.execute_reply":"2023-05-21T01:27:00.535936Z"},"trusted":true},"execution_count":18,"outputs":[{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)","Cell \u001b[0;32mIn[18], line 12\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Input data files are available in the read-only \"../input/\" directory\u001b[39;00m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;66;03m# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mos\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m dirname, _, filenames \u001b[38;5;129;01min\u001b[39;00m os\u001b[38;5;241m.\u001b[39mwalk(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/kaggle/input\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m filename \u001b[38;5;129;01min\u001b[39;00m filenames:\n\u001b[1;32m     14\u001b[0m         \u001b[38;5;28mprint\u001b[39m(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(dirname, filename))\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:419\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    414\u001b[0m         \u001b[38;5;66;03m# Issue #23605: os.path.islink() is used instead of caching\u001b[39;00m\n\u001b[1;32m    415\u001b[0m         \u001b[38;5;66;03m# entry.is_symlink() result during the loop on os.scandir() because\u001b[39;00m\n\u001b[1;32m    416\u001b[0m         \u001b[38;5;66;03m# the caller can replace the directory entry during the \"yield\"\u001b[39;00m\n\u001b[1;32m    417\u001b[0m         \u001b[38;5;66;03m# above.\u001b[39;00m\n\u001b[1;32m    418\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m followlinks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m islink(new_path):\n\u001b[0;32m--> 419\u001b[0m             \u001b[38;5;28;01myield from\u001b[39;00m _walk(new_path, topdown, onerror, followlinks)\n\u001b[1;32m    420\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    421\u001b[0m     \u001b[38;5;66;03m# Recurse into sub-directories\u001b[39;00m\n\u001b[1;32m    422\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m new_path \u001b[38;5;129;01min\u001b[39;00m walk_dirs:\n","File \u001b[0;32m/opt/conda/lib/python3.10/os.py:377\u001b[0m, in \u001b[0;36m_walk\u001b[0;34m(top, topdown, onerror, followlinks)\u001b[0m\n\u001b[1;32m    374\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[1;32m    376\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 377\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[43mentry\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mis_dir\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    378\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mOSError\u001b[39;00m:\n\u001b[1;32m    379\u001b[0m     \u001b[38;5;66;03m# If is_dir() raises an OSError, consider that the entry is not\u001b[39;00m\n\u001b[1;32m    380\u001b[0m     \u001b[38;5;66;03m# a directory, same behaviour than os.path.isdir().\u001b[39;00m\n\u001b[1;32m    381\u001b[0m     is_dir \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n","\u001b[0;31mKeyboardInterrupt\u001b[0m: "],"ename":"KeyboardInterrupt","evalue":"","output_type":"error"}]},{"cell_type":"code","source":"import torch\nimport torchvision\nimport torchvision.transforms as transforms\n\ntransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\nbatch_size = 32\ntrain_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TRAIN\", transform=transform)\ntest_dataset = torchvision.datasets.ImageFolder(\"/kaggle/input/cifar100/CIFAR100/TEST\", transform=transform)\ntrain_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)\ntest_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=2)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T06:54:35.803469Z","iopub.execute_input":"2023-05-21T06:54:35.803869Z","iopub.status.idle":"2023-05-21T06:54:36.214939Z","shell.execute_reply.started":"2023-05-21T06:54:35.803834Z","shell.execute_reply":"2023-05-21T06:54:36.213882Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport numpy as np\n\n# functions to show an image\n\n\ndef imshow(img):\n    img = img / 2 + 0.5     # unnormalize\n    npimg = img.numpy()\n    plt.imshow(np.transpose(npimg, (1, 2, 0)))\n    plt.show()\n\n\n# get some random training images\ndataiter = iter(train_loader)\n# images, labels = dataiter.next()\nimages, labels = next(dataiter)\n\n# show images\nimshow(torchvision.utils.make_grid(images))\n# print labels\nprint(labels.shape)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T06:54:36.619482Z","iopub.execute_input":"2023-05-21T06:54:36.619877Z","iopub.status.idle":"2023-05-21T06:54:37.045845Z","shell.execute_reply.started":"2023-05-21T06:54:36.619845Z","shell.execute_reply":"2023-05-21T06:54:37.044608Z"},"trusted":true},"execution_count":28,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}},{"name":"stdout","text":"torch.Size([32])\n","output_type":"stream"}]},{"cell_type":"code","source":"import math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"3x3 convolution with padding\"\n    return nn.Conv2d(\n        in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False\n    )\n\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n    ):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n      \nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False\n    ):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(\n            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False\n        )\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\nclass Res2NetBottleneck(nn.Module):\n\n    expansion = 4\n\n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4\n    ):\n        super(Res2NetBottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.ModuleList([nn.Conv2d(planes//scales, planes//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn2 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.scales = scales\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        \n#         print(out.shape)\n#         print(\"Begin Forward\")\n        xs = torch.chunk(out, self.scales, 1)\n        ys = []\n        \n        for s in range(self.scales):\n            if s == 0:\n                ys.append(xs[s])\n            elif s == 1:\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))\n            else:\n#                 print(xs[s].shape, ys[-1].shape)\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))\n        out = torch.cat(ys, 1)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out    \n\nclass ColaBasicBlock(nn.Module):\n\n    expansion = 4\n    \n    def __init__(\n        self, inplanes, planes, stride=1, downsample=None, use_triplet_attention=False, scales=4, reduction=4\n    ):\n        super(ColaBasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.reduction = reduction\n        self.mix = nn.ModuleList([nn.Conv2d(planes, planes//self.reduction, kernel_size=1, stride=1, bias=False), \n                                  nn.BatchNorm2d(planes//self.reduction),\n                                  nn.Conv2d(planes//self.reduction, planes, kernel_size=1, stride=1, bias=False),\n                                  nn.BatchNorm2d(planes),\n                                  nn.ReLU(inplace=True)])\n        self.conv2 = nn.ModuleList([nn.Conv2d(planes//scales, planes//scales, kernel_size=3, padding=1, bias=False) for _ in range(scales-1)])\n        self.bn2 = nn.ModuleList([nn.BatchNorm2d(planes // scales) for _ in range(scales-1)])\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n        self.scales = scales\n\n        if use_triplet_attention:\n            self.triplet_attention = TripletAttention(planes * 4, 16)\n        else:\n            self.triplet_attention = None\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n        for block in self.mix:\n            out = block(out)\n#         print(out.shape)\n#         print(\"Begin Forward\")\n        xs = torch.chunk(out, self.scales, 1)\n        ys = []\n        \n        for s in range(self.scales):\n            if s == 0:\n                ys.append(xs[s])\n            elif s == 1:\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s]))))\n            else:\n#                 print(xs[s].shape, ys[-1].shape)\n                ys.append(self.relu(self.bn2[s-1](self.conv2[s-1](xs[s] + ys[-1]))))\n        out = torch.cat(ys, 1)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        if not self.triplet_attention is None:\n            out = self.triplet_attention(out)\n\n        out += residual\n        out = self.relu(out)\n\n        return out    \n    \nclass ResNet(nn.Module):\n    def __init__(self, block, layers, network_type, num_classes, att_type=None):\n        self.inplanes = 64\n        super(ResNet, self).__init__()\n        self.network_type = network_type\n        # different model config between ImageNet and CIFAR\n        if network_type == \"ImageNet\":\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=7, stride=2, padding=3, bias=False\n            )\n            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n            self.avgpool = nn.AvgPool2d(7)\n        else:\n            self.conv1 = nn.Conv2d(\n                3, 64, kernel_size=3, stride=1, padding=1, bias=False\n            )\n\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.layer1 = self._make_layer(block, 64, layers[0], att_type=att_type)\n        self.layer2 = self._make_layer(\n            block, 128, layers[1], stride=2, att_type=att_type\n        )\n        self.layer3 = self._make_layer(\n            block, 256, layers[2], stride=2, att_type=att_type\n        )\n        self.layer4 = self._make_layer(\n            block, 512, layers[3], stride=2, att_type=att_type\n        )\n\n        self.fc = nn.Linear(512 * block.expansion, num_classes)\n\n        init.kaiming_normal_(self.fc.weight)\n        for key in self.state_dict():\n            if key.split(\".\")[-1] == \"weight\":\n                if \"conv\" in key:\n                    init.kaiming_normal_(self.state_dict()[key], mode=\"fan_out\")\n                if \"bn\" in key:\n                    if \"SpatialGate\" in key:\n                        self.state_dict()[key][...] = 0\n                    else:\n                        self.state_dict()[key][...] = 1\n            elif key.split(\".\")[-1] == \"bias\":\n                self.state_dict()[key][...] = 0\n\n    def _make_layer(self, block, planes, blocks, stride=1, att_type=None):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(\n                    self.inplanes,\n                    planes * block.expansion,\n                    kernel_size=1,\n                    stride=stride,\n                    bias=False,\n                ),\n                nn.BatchNorm2d(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(\n            block(\n                self.inplanes,\n                planes,\n                stride,\n                downsample,\n                use_triplet_attention=att_type == \"TripletAttention\",\n            )\n        )\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(\n                block(\n                    self.inplanes,\n                    planes,\n                    use_triplet_attention=att_type == \"TripletAttention\",\n                )\n            )\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n        if self.network_type == \"ImageNet\":\n            x = self.maxpool(x)\n\n        x = self.layer1(x)\n#         print(\"Begin layer2\")\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        if self.network_type == \"ImageNet\":\n            x = self.avgpool(x)\n        else:\n            x = F.avg_pool2d(x, 4)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n        return x\n\n\ndef ResidualNet(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(BasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n#         model = ResNet(Bottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(BasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n        # model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Bottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Bottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n  \ndef Res2Net(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(Res2NetBottleneck, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(Res2NetBottleneck, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(Res2NetBottleneck, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\ndef ColaNet(network_type, depth, num_classes, att_type):\n\n    assert network_type in [\n        \"ImageNet\",\n        \"CIFAR10\",\n        \"CIFAR100\",\n    ], \"network type should be ImageNet or CIFAR10 / CIFAR100\"\n    assert depth in [18, 34, 50, 101], \"network depth should be 18, 34, 50 or 101\"\n\n    if depth == 18:\n        model = ResNet(ColaBasicBlock, [2, 2, 2, 2], network_type, num_classes, att_type)\n\n    elif depth == 34:\n        model = ResNet(ColaBasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 50:\n        model = ResNet(ColaBasicBlock, [3, 4, 6, 3], network_type, num_classes, att_type)\n\n    elif depth == 101:\n        model = ResNet(ColaBasicBlock, [3, 4, 23, 3], network_type, num_classes, att_type)\n\n    return model\n\ndevice=\"cuda\"\nres2net=Res2Net(\"CIFAR100\",18,100,None).to(device)\nprint(res2net)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T06:54:40.544444Z","iopub.execute_input":"2023-05-21T06:54:40.544887Z","iopub.status.idle":"2023-05-21T06:54:40.946680Z","shell.execute_reply.started":"2023-05-21T06:54:40.544843Z","shell.execute_reply":"2023-05-21T06:54:40.945710Z"},"trusted":true},"execution_count":29,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Res2NetBottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Res2NetBottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"resnet_bottleneck = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_bottleneck)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:16:45.003248Z","iopub.execute_input":"2023-05-21T02:16:45.003595Z","iopub.status.idle":"2023-05-21T02:16:45.404928Z","shell.execute_reply.started":"2023-05-21T02:16:45.003563Z","shell.execute_reply":"2023-05-21T02:16:45.404000Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): Bottleneck(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): Bottleneck(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"resnet_basicblock = ResidualNet(\"CIFAR100\",18,100,None).to(device)\nprint(resnet_basicblock)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:43:13.742246Z","iopub.execute_input":"2023-05-21T02:43:13.742599Z","iopub.status.idle":"2023-05-21T02:43:13.974772Z","shell.execute_reply.started":"2023-05-21T02:43:13.742572Z","shell.execute_reply":"2023-05-21T02:43:13.973855Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): BasicBlock(\n      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): BasicBlock(\n      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n    )\n  )\n  (fc): Linear(in_features=512, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"cola_net = ColaNet(\"CIFAR100\",18,100,None).to(device)\nprint(cola_net)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T06:54:45.892447Z","iopub.execute_input":"2023-05-21T06:54:45.892843Z","iopub.status.idle":"2023-05-21T06:54:46.314705Z","shell.execute_reply.started":"2023-05-21T06:54:45.892812Z","shell.execute_reply":"2023-05-21T06:54:46.313756Z"},"trusted":true},"execution_count":30,"outputs":[{"name":"stdout","text":"ResNet(\n  (conv1): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n  (relu): ReLU(inplace=True)\n  (layer1): Sequential(\n    (0): ColaBasicBlock(\n      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBasicBlock(\n      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer2): Sequential(\n    (0): ColaBasicBlock(\n      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBasicBlock(\n      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer3): Sequential(\n    (0): ColaBasicBlock(\n      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBasicBlock(\n      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (layer4): Sequential(\n    (0): ColaBasicBlock(\n      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n      (downsample): Sequential(\n        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n    )\n    (1): ColaBasicBlock(\n      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (mix): ModuleList(\n        (0): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (2): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n        (3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n        (4): ReLU(inplace=True)\n      )\n      (conv2): ModuleList(\n        (0-2): 3 x Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n      )\n      (bn2): ModuleList(\n        (0-2): 3 x BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      )\n      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n      (relu): ReLU(inplace=True)\n    )\n  )\n  (fc): Linear(in_features=2048, out_features=100, bias=True)\n)\n","output_type":"stream"}]},{"cell_type":"code","source":"import torch.optim as optim\n\ncriterion = nn.CrossEntropyLoss()\n# 这里需要修改优化器\noptimizer = torch.optim.Adam(cola_net.parameters(), lr=0.005)\nprint(len(train_loader))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T06:54:48.406549Z","iopub.execute_input":"2023-05-21T06:54:48.406937Z","iopub.status.idle":"2023-05-21T06:54:48.420440Z","shell.execute_reply.started":"2023-05-21T06:54:48.406908Z","shell.execute_reply":"2023-05-21T06:54:48.419407Z"},"trusted":true},"execution_count":31,"outputs":[{"name":"stdout","text":"1250\n","output_type":"stream"}]},{"cell_type":"code","source":"from tqdm import tqdm\ndef train(net, epoch):\n    net.train()\n    # Loop over each batch from the training set\n    train_tqdm = tqdm(train_loader, desc=\"Epoch \" + str(epoch))\n    for batch_idx, (data, target) in enumerate(train_tqdm):\n        # Copy data to GPU if needed\n        data = data.to(device)\n        target = target.to(device)\n        # Zero gradient buffers\n        optimizer.zero_grad()\n        # Pass data through the network\n        output = net(data)\n        # Calculate loss\n        loss = criterion(output, target)\n        # Backpropagate\n        loss.backward()\n        # Update weights\n        optimizer.step()  # w - alpha * dL / dw\n        train_tqdm.set_postfix({\"loss\": \"%.3g\" % loss.item()})\n    return net\n\ndef validate(net, lossv,top1AccuracyList,top5AccuracyList):\n    net.eval()\n    val_loss = 0\n    top1Correct = 0\n    top5Correct = 0\n    for index,(data, target) in enumerate(test_loader):\n        data = data.to(device)\n        labels = target.to(device)\n        outputs = net(data)\n        val_loss += criterion(outputs, labels).data.item()\n        _, top1Predicted = torch.max(outputs.data, 1)\n        top5Predicted = torch.topk(outputs.data, k=5, dim=1, largest=True)[1]\n        top1Correct += (top1Predicted == labels).cpu().sum().item()\n        label_resize = labels.view(-1, 1).expand_as(top5Predicted)\n        top5Correct += torch.eq(top5Predicted, label_resize).view(-1).cpu().sum().float().item()\n\n    val_loss /= len(test_loader)\n    lossv.append(val_loss)\n\n    top1Acc=100*top1Correct / len(test_loader.dataset)\n    top5Acc=100*top5Correct / len(test_loader.dataset)\n    top1AccuracyList.append(top1Acc)\n    top5AccuracyList.append(top5Acc)\n    \n    print('\\nValidation set: Average loss: {:.4f}, Top1Accuracy: {}/{} ({:.0f}%) Top5Accuracy: {}/{} ({:.0f}%)\\n'.format(\n        val_loss, top1Correct, len(test_loader.dataset), top1Acc,top5Correct, len(test_loader.dataset), top5Acc))\n#     accuracy = 100. * correct.to(torch.float32) / len(testloader.dataset)\n#     accv.append(accuracy)","metadata":{"execution":{"iopub.status.busy":"2023-05-21T06:54:51.927330Z","iopub.execute_input":"2023-05-21T06:54:51.927709Z","iopub.status.idle":"2023-05-21T06:54:51.946210Z","shell.execute_reply.started":"2023-05-21T06:54:51.927680Z","shell.execute_reply":"2023-05-21T06:54:51.940674Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"import time\n\nres2netLossv = []\nres2netTop1AccuracyList = []   # top1准确率列表\nres2netTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    res2net = train(res2net, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(res2net, res2netLossv,res2netTop1AccuracyList,res2netTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))\n","metadata":{"execution":{"iopub.status.busy":"2023-05-21T01:49:13.011430Z","iopub.execute_input":"2023-05-21T01:49:13.011841Z","iopub.status.idle":"2023-05-21T02:06:15.828626Z","shell.execute_reply.started":"2023-05-21T01:49:13.011805Z","shell.execute_reply":"2023-05-21T02:06:15.827292Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [03:14<00:00,  6.43it/s, loss=2.34]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3122, Top1Accuracy: 4021/10000 (40%) Top5Accuracy: 7104.0/10000 (71%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.63]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2263, Top1Accuracy: 4265/10000 (43%) Top5Accuracy: 7338.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.77] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1583, Top1Accuracy: 4519/10000 (45%) Top5Accuracy: 7467.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.59] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1745, Top1Accuracy: 4616/10000 (46%) Top5Accuracy: 7521.0/10000 (75%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [03:14<00:00,  6.44it/s, loss=1.23] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2429, Top1Accuracy: 4666/10000 (47%) Top5Accuracy: 7601.0/10000 (76%)\n\nFinished Training\nTraining complete in 16m 11s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\nresnet_bottleneckLossv = []\nresnet_bottleneckTop1AccuracyList = []   # top1准确率列表\nresnet_bottleneckTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_bottleneck = train(resnet_bottleneck, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_bottleneck, resnet_bottleneckLossv,resnet_bottleneckTop1AccuracyList,resnet_bottleneckTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:28:46.022872Z","iopub.execute_input":"2023-05-21T02:28:46.023917Z","iopub.status.idle":"2023-05-21T02:35:44.185983Z","shell.execute_reply.started":"2023-05-21T02:28:46.023866Z","shell.execute_reply":"2023-05-21T02:35:44.184783Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [01:16<00:00, 16.41it/s, loss=3.08]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 4.4866, Top1Accuracy: 3599/10000 (36%) Top5Accuracy: 6654.0/10000 (67%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [01:15<00:00, 16.48it/s, loss=2.47]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.7050, Top1Accuracy: 3805/10000 (38%) Top5Accuracy: 6840.0/10000 (68%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [01:15<00:00, 16.48it/s, loss=2.54]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3556, Top1Accuracy: 4170/10000 (42%) Top5Accuracy: 7197.0/10000 (72%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [01:16<00:00, 16.37it/s, loss=2.09] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2923, Top1Accuracy: 4282/10000 (43%) Top5Accuracy: 7332.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [01:15<00:00, 16.56it/s, loss=1.12] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.3284, Top1Accuracy: 4342/10000 (43%) Top5Accuracy: 7364.0/10000 (74%)\n\nFinished Training\nTraining complete in 6m 20s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\nresnet_basicblockLossv = []\nresnet_basicblockTop1AccuracyList = []   # top1准确率列表\nresnet_basicblockTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    resnet_basicblock = train(resnet_basicblock, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(resnet_basicblock, resnet_basicblockLossv,resnet_basicblockTop1AccuracyList,resnet_basicblockTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T02:47:46.788808Z","iopub.execute_input":"2023-05-21T02:47:46.789213Z","iopub.status.idle":"2023-05-21T02:51:58.960508Z","shell.execute_reply.started":"2023-05-21T02:47:46.789178Z","shell.execute_reply":"2023-05-21T02:51:58.959190Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [00:43<00:00, 29.07it/s, loss=1.95] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.2133, Top1Accuracy: 4380/10000 (44%) Top5Accuracy: 7424.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [00:43<00:00, 28.84it/s, loss=1.24] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.4084, Top1Accuracy: 4323/10000 (43%) Top5Accuracy: 7323.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [00:42<00:00, 29.22it/s, loss=1.14] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.6218, Top1Accuracy: 4388/10000 (44%) Top5Accuracy: 7352.0/10000 (74%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [00:43<00:00, 28.68it/s, loss=0.478]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.0349, Top1Accuracy: 4320/10000 (43%) Top5Accuracy: 7195.0/10000 (72%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [00:43<00:00, 28.54it/s, loss=0.142] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 3.0746, Top1Accuracy: 4373/10000 (44%) Top5Accuracy: 7311.0/10000 (73%)\n\nFinished Training\nTraining complete in 3m 37s\n","output_type":"stream"}]},{"cell_type":"code","source":"import time\n\ncola_netLossv = []\ncola_netTop1AccuracyList = []   # top1准确率列表\ncola_netTop5AccuracyList = []   # top5准确率列表\nmax_epoch = 5\ntraining_time = 0.0\nfor epoch in range(max_epoch):  # loop over the dataset multiple times\n    start = time.time()\n    cola_net = train(cola_net, epoch)\n    end = time.time()\n    training_time += end-start\n    with torch.no_grad():\n        validate(cola_net, cola_netLossv,cola_netTop1AccuracyList,cola_netTop5AccuracyList)\n\n\n        \nprint('Finished Training')\nprint('Training complete in {:.0f}m {:.0f}s'.format(training_time // 60, training_time % 60))","metadata":{"execution":{"iopub.status.busy":"2023-05-21T07:02:38.811600Z","iopub.execute_input":"2023-05-21T07:02:38.812049Z","iopub.status.idle":"2023-05-21T07:09:26.318427Z","shell.execute_reply.started":"2023-05-21T07:02:38.812014Z","shell.execute_reply":"2023-05-21T07:09:26.316616Z"},"trusted":true},"execution_count":34,"outputs":[{"name":"stderr","text":"Epoch 0: 100%|██████████| 1250/1250 [01:12<00:00, 17.14it/s, loss=1.82]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.1978, Top1Accuracy: 4257/10000 (43%) Top5Accuracy: 7338.0/10000 (73%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 1: 100%|██████████| 1250/1250 [01:13<00:00, 17.07it/s, loss=2.21]\n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.0936, Top1Accuracy: 4602/10000 (46%) Top5Accuracy: 7574.0/10000 (76%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 2: 100%|██████████| 1250/1250 [01:13<00:00, 17.01it/s, loss=1.93] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.0190, Top1Accuracy: 4802/10000 (48%) Top5Accuracy: 7718.0/10000 (77%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 3: 100%|██████████| 1250/1250 [01:14<00:00, 16.71it/s, loss=1.3]  \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.0752, Top1Accuracy: 4717/10000 (47%) Top5Accuracy: 7691.0/10000 (77%)\n\n","output_type":"stream"},{"name":"stderr","text":"Epoch 4: 100%|██████████| 1250/1250 [01:12<00:00, 17.15it/s, loss=1.27] \n","output_type":"stream"},{"name":"stdout","text":"\nValidation set: Average loss: 2.0830, Top1Accuracy: 4770/10000 (48%) Top5Accuracy: 7744.0/10000 (77%)\n\nFinished Training\nTraining complete in 6m 7s\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}